We explore a novel application of facial asymmetry: expression classification. Using 2D facial expression images, we show the effectiveness of automatically selected local facial asymmetry for expression recognition. Quantitative evaluations of expression classification using local asymmetry demonstrate statistically significant improvements over expression classification results on the same data set without explicit representation of facial asymmetry. A comparison of discriminative local facial asymmetry features for expression classification versus human identification is given.
|Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|Published - Oct 19 2004
|Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004 - Washington, DC, United States
Duration: Jun 27 2004 → Jul 2 2004
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition